The subject matter disclosed herein relates generally to motion detecting, and more particularly to systems and methods for motion detecting for medical imaging.
Images of a subject, for example a portion of interest of a patient, may be obtained by a variety of different methods. Such methods include, as examples, single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT). These imaging systems typically form an image by performing one or more data acquisitions performed at discrete time intervals, with an image formed from a combination of the information obtained by the data acquisitions. For example, nuclear medicine (NM) imaging systems, use one or more image detectors to acquire imaging data, such as gamma ray or photon imaging data. The image detectors may be gamma cameras that acquire a view or views of emitted radionuclides (from an injected radioisotope) from a patient being imaged.
Performing a series of image acquisitions may take a considerable amount of time. For example, an image may be generated by data that is acquired over a time period of up to 15 minutes or more. Because the final image is reconstructed from a combination of portions of information obtained over time, any movement by a patient may result in blurring or other artifacts that reduce image quality or usability. However, it is frequently difficult for patients to remain still during the entire image acquisition process or portions thereof. For example, one form of motion frequently encountered in image acquisition is caused by breathing. For a certain imaging (or imaging portions) taking over 45 seconds, it may be difficult for patients to hold their breath that long. Similarly, patients may shift their weight or move in other fashions during image acquisition. Such movement of a patient relative to a detector or detectors results in inconsistencies between sets of data obtained over time relative to each other, and results in blurring or other artifacts. Certain presently known attempts to deal with patient movement are either uncomfortable, fail to provide a measure of magnitude for movement during image acquisition, fail to provide for differentiation and/or characterization of different types of movement or different positions taken by a patient, and/or fail to provide motion information in a timely and efficient manner.
For example, in NM an image may be reconstructed from acquired data. The reconstruction may involve creating a 3D image from a plurality of 2D images which were acquired by the camera. The image quality strongly depends on the duration of the data acquisition, to the point that to create a clinically useful image, about 15 minutes of data acquisition or more may be needed. For example, in certain known techniques, data is acquired with time stems or as a series of timely frames. Then, consecutive frames (of the same view-point) are compared. If motion is detected (by displacement of anatomical markers), the data is corrected by shifting the data in the frame. A final image is reconstructed from the (now corrected) totality of the frames. Or, a set of timely images may be reconstructed, each from frames taken at the same time. The consecutive images are compared. If motion is detected (by displacement of anatomical markers), the image is corrected by shifting the data in the image. A final image is created by summing all the (now corrected) totality of images.
However, such known techniques have a number of drawbacks. For example, the division of the data to time slots is arbitrary (as motion may occur at random times). Thus, it is likely that at least some of the frames includes data of “before & after” the motion. The blur caused by this frame cannot be corrected. Also, if the data is divided to many “time windows,” the quality of the data in the frames (or in the image reconstructed from these frames) is poor. This can lead to a number of errors. For example, motion may not be detected (as poor quality images are attempted to be compared). Also, noise in the image can imitate a motion, thereby causing unnecessary and incorrect shift in the image which in turn leads to blur. Further, the amount of motion is inaccurately assessed due to noise—causing incorrect shift in the image—which leads to some residual blur. If, on the other hand, the data is divided into few “time windows,” the quality of the data in the frames (or in the image reconstructed from these frames) is good, but the likelihood that at least some of the frames includes data of “before & after” the motion increases.